Geneva, Switzerland
September 1-4, 2003

A Neural Network Approach to Dependency Analysis of Japanese Sentences Using Prosodic Information

Kazuyuki Takagi, Mamiko Okimoto, Yoshio Ogawa, Kazuhiko Ozeki

University of Electro-Communications, Japan

Prosody and syntax are significantly related with each other as has
often been observed. In the field of speech synthesis, many efforts
have been made to control prosody so that it reflects the syntactic
structure of the sentence. However, the inverse problem, recovery of
syntactic structure using prosodic information, has not been so much
investigated. This paper focuses on syntactic information contained
in prosodic features extracted from read Japanese sentences, and describes
a method of exploiting it in dependency structure analysis. In this
paper, a multilayer perceptron is employed to estimate conditional
probability of dependency distance of a phrase given its prosodic feature,
i.e., pause duration and F_0 contour. Parsing accuracy was improved
by combining two different kinds of prosodic information by the perceptron.